Oral presentation at STACOM10
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Atlas-based Quantication of Myocardial MotionAbnormalities: Added-value for theUnderstanding of CRT Outcome?
STACOM-CESC Workshop, MICCAI 2010Beijing– 20/09/2010
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1a
Atlas-based quantification of motion abnormalities
Healthy subjects
Radial velocity
Long. velocity
Atlas
variance
average
(mm/s)
(mm/s)
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1b
Atlas-based quantification of motion abnormalities
Radial velocity
Long. velocity
Atlas
d = ???
Healthy subjects
Patient to study
p-value (log scale)
(mm/s)
(mm/s)
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New quantitative indexes [quantification of motion abnormalities]
Statistical atlas Automatic Reproducible
Contributions
2
In this work: Mechanisms involved in CRT response Quantification before and after the therapy
Added-value for clinical studies Accurate, automatic Generic methods applicable to almost any
imaging modality studied parameter and mechanism
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[1] Stellbrink et al. , EHJ Suppl. 2004 [5] Parsai et al., EHJ 2009 [2] Chung et al. , Circulation 2008 [6] De Boeck et al., EJHF 2009[3] Fornwalt et al. , JASE 2009 [7] Voigt et al., EHJ 2009[4] Voigt, EHJ 2009
Lack of reproducibility in large scale studies [1]
Is there a “universal” index? [2,3,4]
Changing the strategy?
Patient classification into specific etiologies of HF [5]
Correction of specific mechanisms of dyssynchrony conditions response
Predicitive value of specific classes
• Septal flash [5]
• Septal rebound stretch [6]
• Apical transverse motion [7]
3
Why quantifying abnormalities? CRT context
Need to accurately characterize these patterns
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Fig.3: Septal flash mechanismWhat is a “septal flash” ?
Healthy volunteer CRT candidate with SF
4
Parsai, Bijnens et al., EHJ 2009
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3
Effect of CRT on septal flash
Pre-CRT Follow-up (6 months)
5
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3
Effect of CRT on septal flash
Pre-CRT Follow-up (6 months)
Pre-CRT
5
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3
Effect of CRT on septal flash
Follow-up(6 months)
Pre-CRT Follow-up (6 months)
Pre-CRT
5
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Plan
Atlas pipeline
Relevance of the atlas population
Clinical outcome after CRT
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Healthy subjects
Atlas of “normality”
Registration-based tracking
Spatio-temporal
normalization
Group statistics: average, covariance, …
Myocardial velocities
8a
[9] Duchateau et al. , MICCAI 2009
Construction of an atlas of “normality”[9]
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Healthy subjects
Atlas of “normality”
Registration-based tracking
Group statistics: average, covariance, …
8b
[9] Duchateau et al. , MICCAI 2009
Construction of an atlas of “normality”[9]
Normalized timescaleInital ECG
Temporal normalization
Spatio-temporal
normalization
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Healthy subjects
Atlas of “normality”
Registration-based tracking
Group statistics: average, covariance, …
8c
[9] Duchateau et al. , MICCAI 2009
Construction of an atlas of “normality”[9]
Spatio-temporal
normalization
Spatial reorientation
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Atlas of “normality”
Population of CRT
candidates
9
Statistical distance to “normality”
Healthy subjects
d = ???
Statistical distance = p-value associated to Mahalanobis distance
LOW p-value = HIGH abnormality
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Plan
Atlas pipeline
Relevance of the atlas population
Clinical outcome after CRT
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2D echo, 4-chamber view
11
Data available
21 Healthy volunteers
60 frames/s0.24 x 0.24 mm2
88 candidates OFF / ON / FU (11+/- 2 months)EF < 35%, QRS duration > 120ms, and (or) NYHA class III-IV
60 frames/s0.24 x 0.24 mm2
CRT response:Clinical 6min walking test increase ≥ 10%
Echocardiographic LV end-systolic volume reduction ≥ 15% or NYHA class reduction ≥ 1 point
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13
Is the atlas representative of “normality”?
• Non-dilated hearts• No antecedent of cardiac dysfunction• Normal baseline characteristics• Young (30 +/- 5)
How many subjects?
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14
Is the atlas representative of “normality”?
• Non-dilated hearts• No antecedent of cardiac dysfunction• Normal baseline characteristics• Young (30±5)
How many subjects?
Statistical distribution assumption
d = ???
Statistical distance = p-value associated to Mahalanobis distance
Gaussianity tests:Shapiro-Wilk (SW) and Lilliefors (LF)
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Plan
Atlas pipeline
Relevance of the atlas population
Clinical outcome after CRT
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Inward Outward
10a
Data representation
Temporal evolution at a fixed anatomical point
p-value (log scale)
Local maps at fixed time t
p-value (log scale)
Red = large abnormality
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Data representation
Spatiotemporal maps of abnormality
Blue = Inward (vp<0)Red = Outward (vp>0)
Base
ApexTime
IVC Systole Diastole
Inward Outward
10b
p-value (log scale)
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Spatiotemporal quantification of abnormalities
CRT #9Septal flash
CRT #8Septal flash
CRT #12Left-right
interaction
15a
Blue = Inward (vp<0)Red = Outward (vp>0)
Local p-value * sign of radial velocity
(log scale)
???
IVC Systole Diastole
OFF
Follow-up
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Spatiotemporal quantification of abnormalities
CRT #9Septal flash
CRT #8Septal flash
CRT #12Left-right
interaction
15b
Blue = Inward (vp<0)Red = Outward (vp>0)
IVC Systole Diastole
OFF Follow-upLocal p-value * sign of
radial velocity(log scale)
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Reduction of specific abnormalities (SF)
17
Correction of SF = High predictive value
p-value(log scale)
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Conclusions
18
Added-value for clinical studies Accurate, automatic Information still available at every location (x,t) [not heart segments only] Generic methods applicable to almost any
imaging modality studied parameter and mechanism
Clinical conclusionsSimilar observations than in previous clinical studies [5,6]
Observation of global abnormalities leads to limited conclusions
Correction of specific abnormalities (e.g. SF) = high predictor of response
Further work = extension to strain measurements (influence of local infarction)
[5] Parsai et al., EHJ 2009[6] Parsai et al., EHJ 2009
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CISTIB, Universitat Pompeu Fabra Image registration team M. De Craene, G. Piella
Hospital Clínic, Barcelona E. Silva, A. Doltra, M. Sitges, B. H. Bijnens
Acknowledgements
Related worksAtlas construction: N. Duchateau, M. De Craene, E. Silva, M. Sitges, B. H. Bijnens, and A. F. Frangi “Septal Flash Assessment on CRT Candidates based on Statistical Atlases of Motion” MICCAI’09 LNCS 5762 (pp.759-766)
Quantification of CRT outcome: N. Duchateau, A. Doltra, E. Silva, M. De Craene, G. Piella, L. Mont, Ma A. Castel, J. Brugada, M. Sitges, and A. F. Frangi “Added value of a statistical atlas-based quantification of motion abnormalities for the prediction of CRT response” EuroEcho 2010 Randers – Lecture rooms – 09/12/2010
19